Therapeutic zone assessor

ABSTRACT

Systems and methods are provided for identifying therapeutic zones where there is glycemic dysfunction of a specific type that can be addressed by making strategic changes to behavior and/or therapy parameters. Systems and methods described herein evaluate large historical data sets to: identify a therapeutic zone or zones with glycemic dysfunction that are most readily addressable; quantify the glycemic impact of a plurality of different therapeutic adjustments in terms of either adjustments to historical doses or the parameters of a prospective dosing strategy to determine the highest possible improvement; and/or identify patient dosing strategies to provide therapy recommendations adapted for the patient&#39;s preferred behavioral dosing strategy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/950,029, filed on Dec. 18, 2019, entitled“THERAPEUTIC ZONE ASSESSOR,” the contents of which are herebyincorporated by reference in its entirety, and is hereby expressly madea part of this specification.

BACKGROUND

With the growing adoption of CGM (continuous glucose monitoring) andconnected devices, the availability and reliability of glucosetime-series data has increased in recent years.

Identifying multiple patterns in large historic data sets requires alevel of complexity that cannot be addressed by human evaluation due atleast in part to overlapping symptoms in those patterns. Even withpattern analysis tools, doctors cannot reliably determine what aspect ofdiabetes therapy is most readily addressable for each unique patientsituation based on a review of the data.

Because of the numerous variables and factors involved in diabetesmanagement, current practices for identifying patterns and makingrecommendations lack a reliability and ease of use that considers thingssuch as credibility of data, therapeutic addressability of certainaspects of diabetes risks, impact improvements, patient-preferreddiabetes management strategies, and synthesis of related risks andtherapies, for example.

Often times, clinicians review CGM traces for a patient over a period oftime, such as 14 days, and corresponding insulin delivery patterns, orreview the data in a more consolidated format such as a plot that showseach of the 14 days of data overlaid on a 24 hour timeline in an attemptto visually highlight areas of stronger patterns within a particulartime of day.

However, visualization of the data cannot easily highlight many of theimportant factors, risks, and potential outcomes needed for effectivetherapy optimization. Additionally, the credibility of the data isunknown or unclear from visual inspection.

It is with respect to these and other considerations that the variousaspects and embodiments of the present disclosure are presented.

SUMMARY

Systems and methods are provided for identifying therapeutic zones wherethere is glycemic dysfunction of a specific type that can be addressedby making strategic changes to behavior and/or therapy parameters.

Systems and methods described herein evaluate large historical data setsto: identify a therapeutic zone or zones with glycemic dysfunction thatare most readily addressable; quantify the glycemic impact of aplurality of different therapeutic adjustments in terms of eitheradjustments to historical doses or the parameters of a prospectivedosing strategy to determine the highest possible improvement; and/oridentify patient dosing strategies to provide therapy recommendationsadapted for the patient's preferred behavioral dosing strategy.

In an implementation, a method comprises: deriving at least one singlesymptom-specific risk profile, using a glycemic risk profiler;determining, using a therapeutic zone assessor, at least onetherapeutically correlated zone associated with the at least one singlesymptom-specific risk profile; determining, using a zone importancequantifier, an importance value of the at least one therapeuticallycorrelated zone; and outputting information based on the importancevalue.

Implementations may include some or all of the following features. Themethod further comprises receiving glucose data, wherein deriving the atleast one single symptom-specific risk profile uses the glucose data.The glucose data comprises at least one of CGM (continuous glucosemonitoring) readings, confidence readings assigned to CGM values,self-monitoring blood glucose readings, or retrospectively calibrated orcorrected CGM readings. The glucose data encompasses a time period of atleast one week. The at least one single symptom-specific risk profiledescribes either hypoglycemic risk or hyperglycemic risk as a functionof the time of day using glucose data. Deriving the at least one singlesymptom-specific risk profile comprises at least one of evaluatingsteepness (first and second order derivatives of a curve), frequency,severity, curvature, average value of profile across 24 hours, orvariability of the profile (mean and standard deviation). The at leastone single symptom-specific risk profile is indicative of glycemicdysfunction based the CGM signal over a selected time period, indicatingrecurring windows of time characterized by a predefined severity andfrequency of hypoglycemia or hyperglycemia over the selected timeperiod. The at least one single symptom-specific risk profile representsat least one of hypoglycemia isolated from hyperglycemia, orhyperglycemia isolated from hypoglycemia. Determining the at least onetherapeutically correlated zone comprises identifying the at least onetherapeutically correlated zone from the at least one singlesymptom-specific risk profile. The at least one therapeuticallycorrelated zone is an interval of a 24 hour day in which BG data of apatient indicates that at least one of the insulin basal rate or dose orbolus strategies of the patient are systematically non-optimal. The atleast one therapeutically correlated zone is identified and associatedwith at least one risk profile and comprises at least one interval ofthe day in which one or more single symptom-specific risk profilesindicate potential glycemic dysfunction.

Implementations may also include some or all of the following features.The method further comprises identifying a period of time in which theat least one single symptom-specific risk profile could be mitigated viathe adjustment of parameters or timing of insulin therapy. Determiningthe at least one therapeutically correlated zone is based on which ofleast one of candidate behavioral changes or therapeutic changes arepredicted to decrease a single-symptom glycemic risk without asubsequent increase in another symptom. Determining the importance valueof the at least one therapeutically correlated zone comprisesprioritizing the zone that is more therapeutically significant oraddressable. Determining the importance value of the at least onetherapeutically correlated zone comprises evaluating a magnitude of arisk. Determining the importance value of the at least onetherapeutically correlated zone comprises considering at least one ofthe time of day or proximity of one risk profile to another riskprofile. The importance value is the peak value of the at least onesingle symptom-specific risk profile. Deriving the at least one singlesymptom-specific risk profile is based on data above a particularcredibility level. Outputting the information comprises outputting atleast one of numerical, alphanumerical, or graphical information.Outputting the information comprises outputting at least one ofbehavioral changes or therapeutic changes to a therapeutic zone of timeto decrease a single symptom in a time window. Outputting theinformation comprises outputting the information to a connected insulinpump or insulin pen, or into a bolus calculator. Outputting theinformation comprises outputting a graphical representation of at leastone of risk profiles or therapeutically correlated zones, or relativeimportance of at least one of risk profiles or therapeuticallycorrelated zones.

In an implementation, a system comprises: a glycemic risk profilerconfigured to derive at least one single symptom-specific risk profile;a therapeutic zone assessor configured to determine at least onetherapeutically correlated zone associated with the at least one singlesymptom-specific risk profile; a zone importance quantifier configuredto determine an importance value of the at least one therapeuticallycorrelated zone; and a therapeutic zone report generator configured tooutput information based on the importance value.

Implementations may include some or all of the following features. Theglycemic risk profiler is further configured to receive glucose data,wherein deriving the at least one single symptom-specific risk profileuses the glucose data. The glucose data comprises at least one of CGM(continuous glucose monitoring) readings, confidence readings assignedto CGM values, self-monitoring blood glucose readings, orretrospectively calibrated or corrected CGM readings. The glucose dataencompasses a time period of at least one week. The at least one singlesymptom-specific risk profile describes either hypoglycemic risk orhyperglycemic risk as a function of the time of day using glucose data.Deriving the at least one single symptom-specific risk profile comprisesat least one of evaluating steepness (first and second order derivativesof a curve), frequency, severity, curvature, average value of profileacross 24 hours, or variability of the profile (mean and standarddeviation). The at least one single symptom-specific risk profile isindicative of glycemic dysfunction based the CGM signal over a selectedtime period, indicating recurring windows of time characterized by apredefined severity and frequency of hypoglycemia or hyperglycemia overthe selected time period. The at least one single symptom-specific riskprofile represents at least one of hypoglycemia isolated fromhyperglycemia, or hyperglycemia isolated from hypoglycemia. Determiningthe at least one therapeutically correlated zone comprises identifyingthe at least one therapeutically correlated zone from the at least onesingle symptom-specific risk profile. The at least one therapeuticallycorrelated zone is an interval of a 24 hour day in which BG data of apatient indicates that at least one of the insulin basal rate or dose orbolus strategies of the patient are systematically non-optimal. The atleast one therapeutically correlated zone is identified and associatedwith at least one risk profile and comprises at least one interval ofthe day in which one or more single symptom-specific risk profilesindicate potential glycemic dysfunction.

Implementations may also include some or all of the following features.The therapeutic zone assessor is further configured to identify a periodof time in which the at least one single symptom-specific risk profilecould be mitigated via the adjustment of parameters or timing of insulintherapy. Determining the at least one therapeutically correlated zone isbased on which of least one of candidate behavioral changes ortherapeutic changes are predicted to decrease a single-symptom glycemicrisk without a subsequent increase in another symptom. Determining theimportance value of the at least one therapeutically correlated zonecomprises prioritizing the zone that is more therapeutically significantor addressable. Determining the importance value of the at least onetherapeutically correlated zone comprises evaluating a magnitude of arisk. Determining the importance value of the at least onetherapeutically correlated zone comprises considering at least one ofthe time of day or proximity of one risk profile to another riskprofile. The importance value is the peak value of the at least onesingle symptom-specific risk profile. Deriving the at least one singlesymptom-specific risk profile is based on data above a particularcredibility level. Outputting the information comprises outputting atleast one of numerical, alphanumerical, or graphical information.Outputting the information comprises outputting at least one ofbehavioral changes or therapeutic changes to a therapeutic zone of timeto decrease a single symptom in a time window. Outputting theinformation comprises outputting the information to a connected insulinpump or insulin pen, or into a bolus calculator. Outputting theinformation comprises outputting a graphical representation of at leastone of risk profiles or therapeutically correlated zones, or relativeimportance of at least one of risk profiles or therapeuticallycorrelated zones.

In an implementation, a system comprises: at least one processor; and anon-transitory computer readable medium comprising instructions that,when executed by the at least one processor, cause the system to: deriveat least one single symptom-specific risk profile; determine at leastone therapeutically correlated zone associated with the at least onesingle symptom-specific risk profile; determine an importance value ofthe at least one therapeutically correlated zone; and output informationbased on the importance value.

Implementations may include some or all of the following features. Thesystem further comprises instructions that, when executed by the atleast one processor, cause the system to receive glucose data, whereinderiving the at least one single symptom-specific risk profile uses theglucose data. The glucose data comprises at least one of CGM (continuousglucose monitoring) readings, confidence readings assigned to CGMvalues, self-monitoring blood glucose readings, or retrospectivelycalibrated or corrected CGM readings. The glucose data encompasses atime period of at least one week. The at least one singlesymptom-specific risk profile describes either hypoglycemic risk orhyperglycemic risk as a function of the time of day using glucose data.Deriving the at least one single symptom-specific risk profile comprisesat least one of evaluating steepness (first and second order derivativesof a curve), frequency, severity, curvature, average value of profileacross 24 hours, or variability of the profile (mean and standarddeviation). The at least one single symptom-specific risk profile isindicative of glycemic dysfunction based the CGM signal over a selectedtime period, indicating recurring windows of time characterized by apredefined severity and frequency of hypoglycemia or hyperglycemia overthe selected time period. The at least one single symptom-specific riskprofile represents at least one of hypoglycemia isolated fromhyperglycemia, or hyperglycemia isolated from hypoglycemia. Determiningthe at least one therapeutically correlated zone comprises identifyingthe at least one therapeutically correlated zone from the at least onesingle symptom-specific risk profile. The at least one therapeuticallycorrelated zone is an interval of a 24 hour day in which BG data of apatient indicates that at least one of the insulin basal rate or dose orbolus strategies of the patient are systematically non-optimal. The atleast one therapeutically correlated zone is identified and associatedwith at least one risk profile and comprises at least one interval ofthe day in which one or more single symptom-specific risk profilesindicate potential glycemic dysfunction.

Implementations may also include some or all of the following features.The system further comprises instructions that, when executed by the atleast one processor, cause the system to identify a period of time inwhich the at least one single symptom-specific risk profile could bemitigated via the adjustment of parameters or timing of insulin therapy.Determining the at least one therapeutically correlated zone is based onwhich of least one of candidate behavioral changes or therapeuticchanges are predicted to decrease a single-symptom glycemic risk withouta subsequent increase in another symptom. Determining the importancevalue of the at least one therapeutically correlated zone comprisesprioritizing the zone that is more therapeutically significant oraddressable. Determining the importance value of the at least onetherapeutically correlated zone comprises evaluating a magnitude of arisk. Determining the importance value of the at least onetherapeutically correlated zone comprises considering at least one ofthe time of day or proximity of one risk profile to another riskprofile. The importance value is the peak value of the at least onesingle symptom-specific risk profile. Deriving the at least one singlesymptom-specific risk profile is based on data above a particularcredibility level. Outputting the information comprises outputting atleast one of numerical, alphanumerical, or graphical information.Outputting the information comprises outputting at least one ofbehavioral changes or therapeutic changes to a therapeutic zone of timeto decrease a single symptom in a time window. Outputting theinformation comprises outputting the information to a connected insulinpump or insulin pen, or into a bolus calculator. Outputting theinformation comprises outputting a graphical representation of at leastone of risk profiles or therapeutically correlated zones, or relativeimportance of at least one of risk profiles or therapeuticallycorrelated zones.

In an implementation, a method comprises: receiving glucose and insulindata; identifying a therapeutic improvement opportunity using theglucose and insulin data; determining candidate changes to insulintherapy; assessing an improvement in therapeutic risk based on thecandidate changes; quantifying the improvement of the candidate changes;and outputting at least one of the candidate changes based on theimprovement.

Implementations may include some or all of the following features. Theglucose and insulin data is received from at least one of a patient or aconnected system or device. Identifying the therapeutic improvementopportunity comprises receiving a user selection of at least one of amealtime, a time of day, or a parameter setting. The parameter settingis a carb ratio. The candidate changes to insulin therapy comprisepercentage increases or decreases to bolus therapy or basal therapy. Thecandidate changes to insulin therapy comprise changes to insulindelivery parameters associated with bolus therapy or basal therapy. Thecandidate changes are in terms of carb ratios, correction factors, basalrates, or profiles. The candidate changes comprise basal dosesensitivity. The candidate changes comprise percentage change to basalor bolus doses in therapeutic zones. Quantifying the improvement of thecandidate changes comprises comparing risk profile values. Outputting atleast one of the candidate changes based on the improvement comprisesoutputting the candidate change that provides the optimized riskprofile. Outputting at least one of the candidate changes comprisesproviding an output in the form of a graph illustrating at least one ofa candidate change or an optimized risk output to a user interface orconnected device. The connected device comprises a bolus calculator. Theoutput is provided by a natural language processor to describe acandidate change and an optimized risk outcome. The output identifieswhich therapeutic zones or zone groups have been optimized.

In an implementation, a system comprises: a therapeutic improvementidentifier configured to evaluate collated glucose and insulin data of apatient to identify areas for therapy optimization in a diabetesmanagement routine of the patient, and to generate a therapeuticimprovement; a relative insulin optimizer configured to propose changesto a therapy, assess the impact of the changes, and quantifies animprovement associated with the changes; and a relative insulinoptimizer report generator that provides an output.

Implementations may include some or all of the following features. Therelative insulin optimizer comprises: a change proposer configured topropose the changes to insulin therapy; an impact assessor configured toassess the impact of candidate therapy changes by estimating the impactto a risk profile of historical glucose values; and an improvementquantifier configured to quantify an improvement of candidate therapychanges. The change proposer is further configured to propose thechanges as percentage-wise changes to at least one of basal or bolus ina time window. The improvement quantifier is configured to quantify theimprovement of candidate therapy changes, based on a percentageimprovement or change in blood glucose outcome metrics. The relativeinsulin optimizer report generator is configured to output candidatetherapy change to a user. The user is one of a clinician, a patient, ora connected device or system. The therapeutic improvement identifiercomprises a user selection of a therapy or a time of day to beoptimized. The user is a patient or a clinician. The therapeuticimprovement is identified by an algorithm.

In an implementation, a system comprises: at least one processor; and anon-transitory computer readable medium comprising instructions that,when executed by the at least one processor, cause the system to:receive glucose and insulin data; identify a therapeutic improvementopportunity using the glucose and insulin data; determine candidatechanges to insulin therapy; assess an improvement in therapeutic riskbased on the candidate changes; quantify the improvement of thecandidate changes; and output at least one of the candidate changesbased on the improvement.

Implementations may include some or all of the following features. Theglucose and insulin data is received from at least one of a patient or aconnected system or device. Identifying the therapeutic improvementopportunity comprises receiving a user selection of at least one of amealtime, a time of day, or a parameter setting. The parameter settingis a carb ratio. The candidate changes to insulin therapy comprisepercentage increases or decreases to bolus therapy or basal therapy. Thecandidate changes to insulin therapy comprise changes to insulindelivery parameters associated with bolus therapy or basal therapy. Thecandidate changes are in terms of carb ratios, correction factors, basalrates, or profiles. The candidate changes comprise basal dosesensitivity. The candidate changes comprise percentage change to basalor bolus doses in therapeutic zones. Quantifying the improvement of thecandidate changes comprises comparing risk profile values. Outputting atleast one of the candidate changes based on the improvement comprisesoutputting the candidate change that provides the optimized riskprofile. Outputting at least one of the candidate changes comprisesproviding an output in the form of a graph illustrating at least one ofa candidate change or an optimized risk output to a user interface orconnected device. The connected device comprises a bolus calculator. Theoutput is provided by a natural language processor to describe acandidate change and an optimized risk outcome. The output identifieswhich therapeutic zones or zone groups have been optimized.

In an implementation, a method comprises: receiving at least one ofglucose data, insulin data, or other-diabetes related data of a patient;identifying a therapeutic improvement opportunity using the at least oneof glucose data, insulin data, or other-diabetes related data;identifying an insulin dosing strategy of the patient; scoring theinsulin dosing strategy for patient compliance; performing optimizationfor the insulin dosing strategy; and providing an output comprisingoptimized insulin strategy parameters to a user.

Implementations may include some or all of the following features. Theother diabetes-related data comprises at least one of meal information,specificity of meals, timing of meals, sizing of meals, carbohydrateestimates, composition information, or exercise information. The atleast one of glucose data, insulin data, or other-diabetes related datais received from at least one of a patient or a connected system ordevice. Identifying the therapeutic improvement opportunity comprisesreceiving a user selection of at least one of a mealtime, a time of day,or a parameter setting. The parameter setting is a carb ratio. Theinsulin dosing strategy comprises a diabetes management or insulinstrategy being implemented by the patient in practice as determined fromthe at least one of glucose data, insulin data, or other-diabetesrelated data of a patient. Performing optimization for the insulindosing strategy determines whether the patient adheres to a knowninsulin strategy and analyzes the effect of percentage changes to theparameters of the identified insulin strategy. The user is at least oneof a clinician, a patient, or a connected device or system. The outputis provided by a natural language processor to describe a candidatechange and an optimized risk outcome. Providing the output comprisesproviding an output in the form of a graph illustrating the optimizedinsulin strategy parameters to a user interface or connected device. Theconnected device comprises a bolus calculator.

In an implementation, a system comprises: a therapeutic improvementidentifier configured to evaluate collated glucose and insulin data of apatient to identify areas for therapy optimization in a diabetesmanagement routine of the patient, and to generate a therapeuticimprovement; an insulin strategy optimizer configured to determinewhether the patient adheres to a known insulin strategy and analyze theeffect of percentage changes to the parameters of the identified insulinstrategy; and a therapy identifier optimizer report generator thatprovides an output.

Implementations may include some or all of the following features. Theinsulin strategy optimizer comprises: an insulin strategy identifierconfigured to identify a diabetes management or insulin strategy beingimplemented by the patient in practice as determined from the collatedglucose and insulin data; a compliance scorer configured to quantify acompliance of the patient with the identified insulin strategy; and aninsulin strategy optimizer within identified behavior configured toperform optimization for the identified insulin strategy. The diabetesdata comprises insulin data and meal data. The insulin strategyidentifier is configured to identify patterns in dosing andcharacterizes the identified insulin strategy of the patient basedthereon. The insulin strategy is the behavioral methodology that thepatient applies in diabetes management, comprising at least one of typesof insulin pump usage, multiple daily injections, or type 2 therapies.The compliance scorer is configured to generate a score computed for adegree of compliance of the patient with the identified insulinstrategy. The insulin strategy optimizer within identified behavior isconfigured to iteratively propose percentage changes to the parametersof the strategy in a selected therapeutic zone or zone group. The outputcomprises optimized insulin strategy parameters. The therapy identifieroptimizer report generator is configured to output a candidate therapychange to a user. The user is at least one of a clinician, a patient, ora connected device or system. The output is provided by a naturallanguage processor to describe a candidate change and an optimized riskoutcome.

In an implementation, a system comprises: at least one processor; and anon-transitory computer readable medium comprising instructions that,when executed by the at least one processor, cause the system to:receive at least one of glucose data, insulin data, or other-diabetesrelated data of a patient; identify a therapeutic improvementopportunity using the at least one of glucose data, insulin data, orother-diabetes related data; identify an insulin dosing strategy of thepatient; score the insulin dosing strategy for patient compliance;perform optimization for the insulin dosing strategy; and provide anoutput comprising optimized insulin strategy parameters to a user.

Implementations may include some or all of the following features. Theother diabetes-related data comprises at least one of meal information,specificity of meals, timing of meals, sizing of meals, carbohydrateestimates, composition information, or exercise information. The atleast one of glucose data, insulin data, or other-diabetes related datais received from at least one of a patient or a connected system ordevice. Identifying the therapeutic improvement opportunity comprisesreceiving a user selection of at least one of a mealtime, a time of day,or a parameter setting. The parameter setting is a carb ratio. Theinsulin dosing strategy comprises a diabetes management or insulinstrategy being implemented by the patient in practice as determined fromthe at least one of glucose data, insulin data, or other-diabetesrelated data of a patient. Performing optimization for the insulindosing strategy determines whether the patient adheres to a knowninsulin strategy and analyzes the effect of percentage changes to theparameters of the identified insulin strategy. The user is at least oneof a clinician, a patient, or a connected device or system. The outputis provided by a natural language processor to describe a candidatechange and an optimized risk outcome. Providing the output comprisesproviding an output in the form of a graph illustrating the optimizedinsulin strategy parameters to a user interface or connected device. Theconnected device comprises a bolus calculator.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating theembodiments, there is shown in the drawings example constructions of theembodiments; however, the embodiments are not limited to the specificmethods and instrumentalities disclosed. In the drawings:

FIG. 1 is a high level functional block diagram of an embodiment of theinvention;

FIG. 2 is a system diagram of an implementation of a therapeutic zoneidentifier;

FIG. 3 is a flow diagram for a method of identifying therapeutic zoneswith potential for improving glycemic outcomes;

FIG. 4 is a diagram that illustrates one example of CGM data formultiple days overlaid on a 24 hour time window from one patient withtype 1 diabetes;

FIG. 5 is a diagram that illustrates one example of singlesymptom-specific risk profile based on a glycemic risk quantifier;

FIG. 6 is a graph that illustrates one example of identified therapeuticzones derived from the single symptom-specific risk profiles;

FIG. 7 is a system diagram of an implementation of a relative insulinoptimizer;

FIG. 8 is a flow diagram for a method of selecting, assessing, andimpacting candidate insulin therapy changes for a patient;

FIGS. 9A and 9B are graphs showing glucose and insulin data,respectively, for a patient over a period of time;

FIG. 10 is a plot showing a visualization of CGM data overlaid for each24 hour day;

FIG. 11 is a graph showing credibility of the CGM and insulin data as afunction of time of day for the data shown FIGS. 9A, 9B, and 10;

FIG. 12 is a chart that shows a risk profile for a patient over a windowof time in some embodiments;

FIGS. 13A and 13B are charts that show actionable and unactionable risk,respectively, as a function of time of day;

FIG. 14 is a chart, for an embodiment of the invention, that illustratesglycemic risk profiles and corresponding therapeutic zones determinedfrom the risk profiles described with reference to FIGS. 12, 13A, and13B;

FIG. 15 is a system diagram of an implementation of an insulin strategyoptimizer;

FIG. 16 is a flow diagram for a method of identifying, scoring, andoptimizing a patient's insulin strategy;

FIG. 17 is a chart that shows 10 days of CGM data for a patient;

FIG. 18 is a chart that illustrates the risk profile function outputfrom the analysis of the 10 days of CGM data shown in FIG. 17;

FIG. 19 are charts that illustrate a comparison of risk profiles fromthe historical data and from replay simulation;

FIG. 20 illustrates a risk profile from a replay;

FIG. 21 is a chart showing two hyperglycemic risk profiles;

FIG. 22 is a chart showing two corresponding hyperglycemic therapeuticzones;

FIG. 23 is a chart showing optimized bolus and basal parameters;

FIGS. 24 and 25 illustrate charts that show example risk profiles; and

FIG. 26 shows an exemplary computing environment in which exampleembodiments and aspects may be implemented.

DETAILED DESCRIPTION

The claimed subject matter is described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the claimed subject matter. It may be evident, however,that the claimed subject matter may be practiced without these specificdetails. In other instances, structures and devices are shown in blockdiagram form in order to facilitate describing the claimed subjectmatter.

FIG. 1 is a high level functional block diagram 100 of an embodiment ofthe invention. A processor 130 communicates with an insulin device 110and a glucose monitor 120. The insulin device 110 and the glucosemonitor 120 communicate with a patient 140 to deliver insulin to thepatient 140 and monitor glucose levels of the patient 140, respectively.The processor 130 is configured to perform the calculations and otheroperations and functions described further herein. The insulin device110 and the glucose monitor 120 may be implemented as separate devicesor as a single device, within a single device, or across multipledevices. The processor 130 can be implemented locally in the insulindevice 110, the glucose monitor 120, or as a standalone device (or inany combination of two or more of the insulin device 110, the glucosemonitor 120, or a standalone device). The processor 130 or a portion ofthe system can be located remotely such as within a server or acloud-based system.

Examples of insulin devices, such as the insulin device 110, includeinsulin syringes, external pumps, and patch pumps that deliver insulinto a patient, typically into the subcutaneous tissue. Insulin devices110 also includes devices that deliver insulin by different means, suchas insulin inhalers, insulin jet injectors, intravenous infusion pumps,and implantable insulin pumps. In some embodiments, a patient will usetwo or more insulin delivery devices in combination, for exampleinjecting long-acting insulin with a syringe and using inhaled insulinbefore meals. In other embodiments, these devices can deliver otherdrugs that help control glucose levels such as glucagon, pramlintide, orglucose-like peptide-1 (GLP-1).

Examples of a glucose monitor, such as the glucose monitor 120, includecontinuous glucose monitors that record glucose values at regularintervals, e.g., 1, 5, or 10 minutes, etc. These continuous glucosemonitors can use, for example, electrochemical or optical sensors thatare inserted transcutaneously, wholly implanted, or measure tissuenoninvasively. Examples of a glucose monitor, such as the glucosemonitor 120, also include devices that draw blood or other fluidsperiodically to measure glucose, such as intravenous blood glucosemonitors, microperfusion sampling, or periodic finger sticks. In someembodiments, the glucose readings are provided in near realtime. Inother embodiments, the glucose reading determined by the glucose monitorcan be stored on the glucose monitor itself for subsequent retrieval.

The insulin device 110, the glucose monitor 120, and the processor 130may be implemented using a variety of computing devices such assmartphones, desktop computers, laptop computers, and tablets. Othertypes of computing devices may be supported. A suitable computing deviceis illustrated in FIG. 26 as the computing device 2600 and cloud-basedapplications.

The insulin device 110, the glucose monitor 120, and the processor 130may be in communication through a network. The network may be a varietyof network types including the public switched telephone network (PSTN),a cellular telephone network, and a packet switched network (e.g., theInternet). Although only one insulin device 110, one glucose monitor120, and one processor 130 are shown in FIG. 1, there is no limit to thenumber of insulin devices, glucose monitors, and processors that may besupported. An activity monitor 150 and/or a smartphone 160 may also beused to collect meal and/or activity data from or pertaining to thepatient 140, and provide the meal and/or activity data to the processor130.

The processor 130 may execute an operating system and one or moreapplications. The operating system may control which applications areexecuted by the insulin device 110 and/or the glucose monitor 120, aswell as control how the applications interact with one or more sensors,services, or other resources of the insulin device 110 and/or theglucose monitor 120.

The processor 130 receives data from the insulin device 110 and theglucose monitor 120, as well as from the patient 140 in someimplementations, and may be configured and/or used to perform one ormore of the calculations, operations, and/or functions described furtherherein.

FIG. 2 is a system diagram of an implementation of a therapeutic zoneidentifier 210, which provides a general framework for the therapeuticzone identification system described herein. As shown, the system usesblood glucose data 205, which may be any diabetes data associated with ahost, such as a human, and may include CGM only data, BG (blood glucose)data, or other glucose or diabetes-related data, depending on theimplementation.

The therapeutic zone identifier 210 identifies an interval of the 24hour day in which the patient's BG data suggests that the patient'smedication dose/strategy needs improvement, e.g., the patient's insulinbasal rate/dose and/or bolus strategies are systematically non-optimalor are putting the patient at risk of diabetic complications. In animplementation, the therapeutic zone identifier 210 uses only bloodglucose data (e.g., not insulin data), such as data from the glucosemonitor 120.

The glycemic risk profiler 220 quantifies the experience ofhypoglycemia, hyperglycemia, or both (variable risk) on average acrossmultiple days by quantifying risk as a pattern.

The therapeutic zone assessor 230 evaluates the risk profile andassesses one or more windows of time over which doses could be adjustedfor systematic improvement of glycemic outcomes related to one or morecorrelated features of the risk profile. The time of day is often animportant factor to be considered.

The zone importance quantifier 240 quantifies the relative importance ofthe therapeutic zones.

The report generator 250 provides output 260 in numerical,alphanumerical, and/or graphical information based on the quantifiedimportance of the therapeutically correlated zones.

FIG. 3 is a flow diagram for a method 300 of identifying therapeuticzones with potential for improving glycemic outcomes. The method 300 ofidentifying the therapeutic zones with the potential for improvingglycemic outcomes includes evaluating large historical data sets toidentify a therapeutic zone with glycemic dysfunction that is mostreadily addressable.

At 310, glucose data is received (e.g., from the glucose monitor 120,the patient 140, the activity monitor 150, and/or the smartphone 160, insome implementations). These data typically comprise measurements ofglucose levels including, for example: CGM readings, confidence readingsassigned to the CGM values, self-monitoring blood glucose readings(blood glucose meter), retrospectively calibrated or corrected CGMreadings, and the like. The glucose data generally encompasses aselected time period of at least one week; however, larger data sets mayprovide longer-term patterns. The glucose data provides forretrospective analysis of CGM as a function of time of day (e.g., withina 24 hour day). FIG. 4 is a diagram 400 that illustrates one example ofCGM data for multiple days overlaid on a 24 hour time window from onepatient with type 1 diabetes.

At 320, one or more single symptom-specific risk profiles are derived.More particularly, profiles of single-symptom glycemic dysfunction areassessed, describing either hypoglycemic risk or hyperglycemic risk as afunction of the time of day using glucose data received at 310. Riskprofiles may be derived using any known glycemic risk quantifier, forexample, such as those described in US 2018/0020988 entitled “Method,system and computer readable medium for assessing actionable glycemicrisk”, inventor Stephen D. Patek, which is incorporated by referenceherein in its entirety. A glycemic risk quantifier may evaluate, forexample, steepness (first and second order derivatives of a curve),frequency, severity, curvature, average value of profile across 24hours, variability of the profile (mean and standard deviation), and thelike, all of which could be meaningful in terms of identifying thezones. Glycemic risk quantifiers include anything that attaches a scoreor value to risk, such as low blood glucose index (LBGI) and high bloodglucose index (HBGI). The glycemic risk quantifier may quantify theexperience of hypoglycemia, hyperglycemia, and/or both in historic data.In some implementations, the method addresses multiple types of risk atthe same time of the day or adjacent times of corresponding to theexperience of both hyperglycemia and hypoglycemia at the time of day ondifferent days of the historical record.

In some embodiments, the one or more single symptom-specific riskprofiles are indicative of glycemic dysfunction based the CGM signalover the selected time period, indicating recurring windows of time(recurring at the same time period within each 24 hour period)characterized by a predefined severity and frequency of a single symptom(e.g., hypoglycemia or hyperglycemia) over the selected time periodbased on an analysis of the one or more single symptom-specific signals.

In some embodiments, times of the day are identified with a consistentpattern of hypoglycemia, hyperglycemia, and/or both.

In some embodiments, the single symptom-specific risk profiles representat least one of a) hypoglycemia isolated from hyperglycemia or b)hyperglycemia isolated from hypoglycemia. In an exemplary embodiment,wherein the single symptom is hypoglycemia, the quantifier may evaluatethe risk index value below a threshold and duration above a threshold,for example, average glucose less than or equal to 70 mg/dl for 30minutes. In an exemplary embodiment, wherein the single symptom ishyperglycemia, the quantifier may evaluate the risk index value a abovea threshold and duration above a threshold, for example, average glucosegreater than or equal to 180 mg/dl for 2 hours.

Other single symptom-specific risk profiles could be defined andevaluated, for example, with A1c greater than a threshold, combinationsof health data including diet and exercise as well as cognitivesymptoms, which may be measured using techniques known in the art.

Preferably, single symptom-specific signals are determined within asingle time period within a 24 hour day to avoid mixed glycemicdysfunction. However, other divisions of time, for example weekday vs.weekend, may be evaluated as well. In general, single symptom-specificsignals are those wherein, during a specific time period, there is noconcurrent single symptom-specific signal of a different type of apredefined severity or frequency. For example, wherein a singlehypoglycemia risk profile may not have concurrent hyperglycemia above avalue/duration threshold. FIG. 5 is a diagram 500 that illustrates oneexample of single symptom-specific risk profile 510 based on a riskquantification 520 provided by a glycemic risk quantifier, which wasderived from the glucose data of the patient shown at FIG. 4. The riskprofile 510 is identified based on the magnitude of the risks shown bythe risk quantification 520.

At 330, one or more therapeutically correlated zones associated with theone or more single symptom-specific risk profiles are assessed (e.g.,determined). In other words, therapeutic zones are identified from therisk profiles.

In general, within the context of insulin therapy, a therapeutic zone isan interval of the 24 hour day in which the patient's BG data suggests(e.g., indicates) that the patient's insulin basal rate/dose and/orbolus strategies are systematically non-optimal. Other contexts, inrelation to other therapies, such as glucagon, type 2 drugs, or evenfood, may also have therapeutic zones assessed accordingly as may beappreciated by one skilled in the art.

In some embodiments, therapeutic zones are identified and associatedwith risk profiles which are considered to be intervals of the day inwhich one or more single symptom-specific risk profiles indicatepotential glycemic dysfunction. In some cases, the identifiedtherapeutic zone is associated with the presence of just one symptom,e.g., an isolated period of the day in which there is a risk ofhypoglycemia (or conversely a risk of hyperglycemia). In such cases, thetherapeutic zone can be identified as a window of time, different from(e.g., expanded around, separate from, adjacent to, and/or overlappingwith) the associated glycemic risk profile as the window of time wherethe root cause behavior/therapy change can be made to mitigate theglycemic, e.g., by systematically reducing insulin in an interval of theday (the therapeutic zone) so as to alleviate the correspondinghypoglycemia risk profile (or conversely by systematically increasinginsulin in the therapeutic zone to alleviate a correspondinghyperglycemia risk profile).

In other cases, the therapeutic zone assessor 230 can associate morethan one therapeutically correlated single-symptom risk profiles to oneor more therapeutic zones. For example, the patient's risk profile mayindicate (1) a risk of hyperglycemia in a specific interval of the day(i.e., a hyperglycemia risk profile) with, in another later interval, aperiod of hypoglycemic risk (i.e., a subsequent correlated hypoglycemiarisk profile) or a period of unaddressable risk (i.e., a subsequentcorrelated interval in which the historical data indicates exposure toboth hyperglycemia and hypoglycemia), and the corresponding identifiedtherapeutic zone (or zones) would represent a period (or periods) of theday where the multiple correlated risk profiles could be mitigated viathe adjustment of the parameters or timing of insulin therapy (e.g., bymore effectively addressing the source of the initial hyperglycemiathereby avoiding the circumstances that can promote hypoglycemia in thelater interval of time). In such cases, the associated multiple riskprofiles could be immediately adjacent, or could be separated by periods(intervals of the day) expressing no glycemic risk. In this way,adjacent or subsequent zones may be combined, due to their dependence,and addressed via therapeutic adjustments in a single therapeutic zone.For example, when a pattern of hypoglycemia is consistently followed byhyperglycemia, the risk profiles are therapeutically related, perhapsdue to overcorrections, and addressed by adjustment to the patient'scorrection factor to a single therapeutic zone, sometimes referred to asa singleton, in some embodiments. The prior art fails to consider thepotential for multiple correlated glycemic risks to be addressed throughtherapeutic adjustments within one or more comprehensive therapeuticzones. In contrast, the systems and methods described herein extracttherapeutic meaning by combining risk with therapeutic zones.

FIG. 6 is a graph 600 that illustrates one example of identifiedtherapeutic zones derived from the single symptom-specific risk profilesin FIG. 5, which was derived from the glucose data of the patient's CGMdata shown in FIG. 4.

In some embodiments, assessment of a risk profile to determine atherapeutic zone is more than just a threshold, for example, could besteepness (first and second order derivatives of a curve), frequency,severity, curvature, average value of profile across 24 hours,variability of the profile (mean and standard deviation) and the like,all of which could be meaningful in terms of identifying the zones.

In some embodiments, the therapeutic zones are assessed based on whichcandidate behavioral and/or therapeutic changes are predicted todecrease the single-symptom glycemic risk without a subsequent increasein another symptom. In other words, a therapeutic zone may be considereda zone that may be therapeutically addressed without risk to negativelyaffecting symptoms in adjacent time windows. In some cases, thetherapeutic zones may be characterized by minimum non-symptomatic, mixedsymptomatic, or different single symptomatic signals.

In all cases, each identified therapeutic zone is an interval of the daythat overlaps, and possibly includes, a portion of the window of time ofthe associated the risk profile. The one or more therapeutic zonesrepresent therapeutic root cause of adjacent risk profiles and may beassessed by looking at a time window adjacent immediately prior to, andpotentially overlapping with, the risk profile(s). Taken together, theidentified therapeutic zones define time windows (i.e., intervals of theday) over which addressable portions of the glycemic risk profile can bemitigated, as described in more detail herein.

At 340, the importance of the therapeutically correlated zone isquantified. In some implementations, an importance value is determinedof the therapeutically correlated zone. Thus, the importance of the oneor more therapeutically correlated zones is quantified by the zoneimportance quantifier. The zone importance quantifier prioritizes whichzone is more therapeutically significant or addressable. In someembodiments, the quantifier evaluates the magnitude of the risk, and mayfurther consider the time of day and/or proximity of one risk profile toanother risk profile (e.g., because in some cases two different riskprofiles are related to each other as they are not isolated incidentsand thus one impacts the other). The quantifier may use a mathematicfunction of the risk profile from 320, for example, the peak value ofthe glycemic risk profile, wherein that peak value could be equal to theimportance of that therapeutic zone in one exemplary embodiment.

The quantifier may be derived from the glycemic risk in embodimentswherein only glucose data is available. However, if additional data isavailable, such as insulin, meals, and exercise, the quantifier may alsoconsider these data in its evaluation. In some embodiments, prioritycould be assigned to therapeutic zones based on preferences of solvingproblems. For example, more significance to bolus issues vs. basalissues or vice versa may be assigned by the system or by the user. Insome embodiments, the zone importance is informed by other factors suchcurrent basal-bolus insulin ratio, for example, 50% basal is already toohigh. In some embodiments, wherein insulin data is available and/orinsulin strategy is determined as described in more detail elsewhereherein, the insulin strategy may be used to prioritize thetherapeutically correlated zones.

In some embodiments, the quantifier assesses the opportunity forreducing risk for each of the one or more zones based on candidatechanges to therapy, described in more detail elsewhere herein (e.g.,using replay analysis), wherein the zone importance quantifierprioritizes therapeutic zones based on the opportunity to decrease therisk profile of one zone compared to another zone.

In some embodiments, systems and methods described herein may determineand apply an analysis of the credibility of the data prior todetermining one or more of the data processing steps described herein.For example, the risk profiles derived by the glycemic risk profiler arebased on data above a particular credibility level. In other words,credibility goes into the objective function of the risk profile. Anexample of credibility analysis and application is described in U.S.application Ser. No. 17/096,785, entitled “Joint state estimationprediction that evaluates differences in predicted vs. correspondingreceived data”, filed Nov. 12, 2020, inventor Stephen D. Patek, which isincorporated by reference herein in its entirety.

At 350, numerical, alphanumerical, and/or graphical information based onthe quantified importance of the therapeutically correlated zones isoutputted. Thus, the therapeutic zone report generator outputsnumerical, alphanumerical, and/or graphical information based on thequantified importance of the therapeutically correlated zones. In oneembodiment, a visualization of the correlated zones are simply outputonto a report. In other embodiments, behavioral and/or therapeuticchanges to the therapeutic zone of time to decrease the single symptomin the identified recurring time window are identified and outputted.

In one example, when a hyperglycemia risk profile occurs in the middleof the day, e.g., the risk profile value is above a hyperglycemia riskthreshold from a time zone of noon, and without hypoglycemia around it,then therapeutic zones immediately preceding (and possible overlapping)the noon to 4 PM time window could be assessed. In this case, time ofday could also be an optional input and the possible root cause(s) maybe analyzed using a look up table, decision tree, or the like,suggesting two reasons for hyperglycemia risk: 1) insufficient lunchtimebolusing (e.g., bolus parameters may be off, patient may beunderestimating carbohydrate intake, or patient may not be bolusing atall, etc.); or 2) insufficient basal. Given the two possible root causesin this example, the report may describe two possible therapeutic issuesto address 1) or 2).

Consider another example where hypoglycemia risk is identified andassociated therapeutic zone is assessed before noon every day. There arethree possible root causes that may be outputted: 1) overaggressivebasal in the morning; 2) over-bolused; and/or 3) bolusing too late(accounting for carbs too late).

In an example wherein consistent hypoglycemia is followed byhyperglycemia, a single therapeutic zone may be outputted withcause/effect association of zones. The report generator may mark timesof day where adjustments could be made, for example hyperglycemiafollowed by hypoglycemia, recommending a basal change at one time of dayor changing of bolus parameters affected another time of day. Therecommendations could be prioritized based on user preference/setting,the quantifier, or other insights such as described in more detailelsewhere herein (e.g., relative improvement of candidate changes orpatient's insulin strategy). In combination scenarios, wherein asingleton zone represents therapeutically connected hypoglycemic andhyperglycemic zones, multiple combinations may be analyzed in view ofimpact on glycemic risk profile.

The output may be in the form of a user interface type report, may besent to a connected insulin pump or insulin pen, or may be fed to into abolus calculator. For example, a therapeutic zone associated with a timeof day wherein changes to a carb ratio for meal boluses have beenidentified (and possibly confirmed/validated by user), the boluscalculator may be automatically programmed with a new carb ratio. Otherrecommendations may be more behavioral in nature, for example, arecommendation to bolus at an earlier time (e.g., before meal).

The report generator may generate and output a graphical representationof risk profiles, the therapeutically correlated zones, and/or theirrelative importance for visual inspection. Additionally oralternatively, a natural language generator or text generator may beused to communicate risk profiles, the therapeutically correlated zones,and/or their relative importance.

FIG. 7 is a system diagram of an implementation of a relative insulinoptimizer 710, which provides a general framework for the insulinoptimization in the systems and methods described herein.

Blood glucose and insulin data are received (e.g., from the glucosemonitor 120, the patient 140, the activity monitor 150, and/or thesmartphone 160, in some implementations) and collated as neededdepending on their sourcing, into collated glucose and insulin data 705.

The therapeutic improvement identifier 707 evaluates the collatedglucose and insulin data 705 to identify areas for therapy optimizationin a patient's diabetes management routine. In some embodiments, theidentifier 707 may comprise a user selection from a clinician or patient(e.g., wherein a user identifies a specific therapy or time of day to beoptimized). The user may select a particular mealtime (e.g., lunch), aspecific time of day (e.g., upon waking in the morning), a particularsetting (e.g., carb ratio), or the like. Any parameter or behavior thataffects insulin therapy may be selected. In some embodiments, thetherapeutic improvement 709 is identified by an algorithm, such as thetherapeutic zone identifier 210 described with respect to FIG. 2;however, other algorithms for identifying areas for improvement are alsopossible as may be appreciated by one skilled in the art.

Based on the therapeutic improvement 709 identified, the relativeinsulin optimizer 710 proposes candidate changes to the therapy,assesses the impact of those changes, and quantifies the improvementassociated with those changes.

The change proposer 720 proposes the candidate changes to insulintherapy such as percentage-wise changes to basal and/or bolus in aparticular time window (e.g., zone or zone group).

The impact assessor 730 assesses impact of candidate therapy changes byestimating the impact to the risk profile of the historical glucosevalues.

The improvement quantifier 740 quantifies improvement of candidatetherapy changes, for example, based on a percentage improvement/changein blood glucose outcome metrics.

The relative insulin optimizer report generator 750 provides an output760, such as outputting candidate therapy change to a user (clinician,patient, or connected device/system).

FIG. 8 is a flow diagram for a method 800 of selecting, assessing, andimpacting candidate insulin therapy changes for a patient. The methodsreceive glucose and insulin data received from the patient and/or aconnected device to run optimization algorithms for improved insulintherapy based on quantified improvements associated with candidatechanges to the insulin therapy. In one example, using glucose andinsulin delivery data, using a replay predictive function, the effectsof percentage changes to basal and/or bolus insulin in the identifiedtherapeutic zones are analyzed.

At 810, glucose and insulin data are received from a patient and/orconnected system/device (e.g., from the glucose monitor 120, the patient140, the activity monitor 150, and/or the smartphone 160, in someimplementations). FIGS. 9A and 9B are graphs 900, 950 showing glucoseand insulin data, respectively, for a patient over about 54 days. FIG.10 is a plot 1000 showing a visualization of all CGM data overlaid foreach 24 hour day. FIG. 11 is a graph 1100 showing credibility of the CGMand insulin data as a function of time of day for the data shown in thegraphs immediately above.

At 820, a therapeutic improvement opportunity is identified as describedin more detail elsewhere herein. In some embodiments, the therapeuticimprovement opportunity identification may comprise a user selectionfrom a clinician or patient (e.g., wherein a user identifies a specifictherapy or time of day to be optimized). The user may select aparticular mealtime (e.g., lunch), a specific time of day (e.g., uponwaking in the morning), a particular setting (e.g., carb ratio), or thelike. Any parameter or behavior that affects insulin therapy may beselected. In some embodiments, the improvement is identified by analgorithm, such as the therapeutic zone identifier described in moredetail elsewhere herein, however other algorithms for identifying areasfor improvement are also possible as may be appreciated by one skilledin the art.

FIG. 12 is a chart 1200 that shows a risk profile for a patient over awindow of time in some embodiments. The area 1210 above the zero linerepresents hyperglycemia, and the area 1220 below the zero linerepresents hypoglycemia as a function of time of day.

FIGS. 13A and 13B are charts 1300, 1350 that show actionable andunactionable risk, respectively, as a function of time of day. At around13 hours (about 1 pm, just after lunch), a peak of actionablehyperglycemia risk exists while at the same time of day the level ofunaddressable risk is low.

FIG. 14 is a chart 1400, for an embodiment of the invention, thatillustrates glycemic risk profiles and corresponding therapeutic zonesdetermined from the risk profiles described with reference to FIGS. 12,13A, and 13B. The lines 1410 represent the risk profiles and the lines1420 represent the corresponding therapeutic zones. In this example,noon-2 pm shows actionable hyperglycemia risk indicating 10 AM-noonwould be a candidate window to change to the insulin therapy.

At 830, candidate changes to insulin therapy are proposed (e.g.,determined) during the therapeutic zone. Candidate changes to insulintherapy may comprise percentage increases or decreases to bolus or basaltherapy and/or may include changes to insulin delivery parametersassociated with the bolus or basal therapy.

Once the therapeutic zone is identified, candidate changes to therapycan be proposed directly in terms of, for example, carb ratios,correction factors, basal rates, and/or profiles thereof. One skilled inthe art may appreciate that any parameters used in diabetes managementthat affects diabetes outcomes may be candidates for change. Parametersmay be specific to insulin pumps, bolus calculators or any valueassociated with insulin therapy whether on type 1 or type 2 single ormultiple daily injection therapy, insulin pen therapy, insulin pumptherapy, artificial pancreas therapy, beta cell therapy, and/or anyaspect(s) thereof.

In some implementations, basal dose sensitivity (percentage change basaldoses and/or basal rates) are candidate changes. In some embodiments,candidate changes may include percentage change to basal or bolus dosesin therapeutic zones. Compound changes and/or combination changes mayalso be proposed, for example, two-factor sensitivity (e.g.,differentiated recommendations for basal vs. bolus). Time of day is alsoa factor that may be proposed for basal rates in some embodiments.

At 840, the improvement in overall therapeutic risk based on thecandidate changes is assessed (e.g., determined). In some embodiments, areplay-predictive function, such as described in U.S. application Ser.No. 17/096,785, entitled “Joint state estimation prediction thatevaluates differences in predicted vs. corresponding received data”,filed Nov. 12, 2020, inventor Stephen D. Patek, which is incorporated byreference herein in its entirety, is performed to estimate the impact ofthe candidate changes at the therapeutic zones on historical glucose,and the risk profiling function is run to determine a new risk profilebased on the candidate changes. For example, percentage changes (5%,10%, etc.) to historic boluses and/or basal rates are replayed duringtherapeutic zones and resulting risk profiles are re-assessed.

At 850, the relative improvement of the candidate changes may bequantified. In one embodiment, the risk profile values are compared toquantify improvement.

At 860, the candidate change(s) to insulin therapy that provided themost optimized risk profile(s) is outputted. The output may be in theform of a graph illustrating the candidate change and/or optimized riskoutput to a user interface or connected device, such as a clinicianreport or connected bolus calculator. Additionally or alternatively, theoutput may be provided by a natural language processor to describe thecandidate change and optimized risk outcome. The output may identifywhich therapeutic zones or zone groups have been optimized and, in someembodiments a text generator may be used to communicate the result(s).

Although diabetes data associated with type 1 diabetes has beenillustrated and described herein, the systems and methods may beapplicable to type 2 diabetes herein such as for basal titrationacceleration, to identify and assess the risk of more or less aggressivetype 2 injections (in terms of medication type, medication dosage,and/or time of injection).

FIG. 15 is a system diagram of an implementation of an insulin strategyoptimizer 1510, which provides a general framework for the insulinoptimization in the systems and methods described herein.

Blood glucose and insulin data are received (e.g., from the glucosemonitor 120, the patient 140, the activity monitor 150, and/or thesmartphone 160, in some implementations) and collated as neededdepending on their sourcing, into collated glucose and insulin data1505.

The therapeutic improvement identifier 1507 evaluates the collatedglucose and insulin data 1505 to identify areas for therapy optimizationin a patient's diabetes management routine. In some embodiments, theidentifier 1507 may be a user selection from a clinician or patient(e.g., wherein a user identifies a specific therapy or time of day to beoptimized). The user may select, for example, a particular mealtime(e.g., lunch), a specific time of day (e.g., upon waking in themorning), a particular setting (e.g., carb ratio), or the like. Anyparameter or behavior that affects insulin therapy may be selected. Insome embodiments, the therapeutic improvement 1509 is identified by analgorithm, such as the therapeutic zone identifier 210 described withrespect to FIG. 2; however, other algorithms for identifying areas forimprovement are also possible as may be appreciated by one skilled inthe art.

Using the records of glucose, insulin, and other data, the insulinstrategy optimizer 1510 determines: (1) whether the patient adheres to aknown insulin strategy (e.g., “functional insulin therapy” with pre-mealboluses based on carb counts) and (2) analyzes the effect of percentagechanges to the parameters of the identified insulin strategy (e.g.,carbohydrate ratios, correction factors, basal rates/dose, etc.). Incontrast to the prior art insulin optimizers that make assumptions aboutthe patient's insulin strategy and/or require changes in the patient'sbehavioral insulin strategy, the systems and methods described hereinidentify the patient's current insulin dosing strategy from numerousdifferent strategies to adapt the insulin therapy optimization to thepatient's chosen behavioral insulin strategy matching their actualreal-world strategy. Optimization focused on how the patient thinksabout their insulin treatment (i.e., strategy) allows for morecustomized user recommendations, resulting in increased compliance andimprovement.

FIG. 16 is a flow diagram for a method 1600 of identifying, scoring, andoptimizing a patient's insulin strategy. The methods receive glucose,insulin and other diabetes-related data, such as meal or exercise data,from the patient and/or a connected device. From the patient data,behavioral patterns can be identified to determine how the patientprefers to manage their diabetes. With knowledge of the patient'sinsulin dosing strategy, changes to the amount and/or timing ofstrategy-specific parameters can be evaluated and recommended asdescribed in more detail herein.

At 1610, glucose, insulin, and/or other diabetes-related data isreceived (e.g., from the glucose monitor 120, the patient 140, theactivity monitor 150, and/or the smartphone 160, in someimplementations). The other diabetes-related data may include mealinformation, such as, for example, specificity and/or timing of meals,general or specific sizing of meals, carbohydrate estimates, compositioninformation, and the like. Additionally or alternatively, exerciseinformation may be provided to include, for example, type of exercise,duration, intensity, heart rate, calories burned, and the like.Diabetes-related data may come from a connected device and/or beself-reported. The data may be collated with glucose and insulin data asappreciated by one skilled in the art.

At 1620, a therapeutic improvement opportunity is identified asdescribed in more detail herein. In some embodiments, the therapeuticimprovement opportunity identification may comprise a user selectionfrom a clinician or patient (e.g., wherein a user identifies a specifictherapy or time of day to be optimized). The user may select aparticular mealtime (e.g., lunch), a specific time of day (e.g., uponwaking in the morning), a particular setting (e.g., carb ratio), or thelike. Any parameter or behavior that affects insulin therapy may beselected. In some embodiments, the improvement is identified by analgorithm, such as by the therapeutic zone identifier 210, however otheralgorithms for identifying areas for improvement are also possible asmay be appreciated by one skilled in the art.

At 1630, the patient's insulin dosing strategy is identified. Theinsulin strategy identifier 1520 identifies the diabetesmanagement/insulin strategy being implemented by the patient in practiceas determined from the diabetes data (insulin data and meal data). Thestrategy identifier 1520 may comprise a series of questions for thepatient or selections to be made by the patient. In some embodiments,the identifier 1520 identifies patterns in dosing and characterizes thepatient's insulin strategy based thereon. The identifier 1520 may usethe replay-predictive function (e.g., described in U.S. application Ser.No. 17/096,785, entitled “Joint state estimation prediction thatevaluates differences in predicted vs. corresponding received data”,filed Nov. 12, 2020, inventor Stephen D. Patek, which is incorporated byreference herein in its entirety) or the like, along with the other datarelevant to dosing to attempt to reproduce the patient's historicalinsulin decisions in context.

By “insulin strategy” it is meant the behavioral methodology that thepatient applies in diabetes management, including types of insulin pumpusage, multiple daily injections, and type 2 therapies as may beappreciated by one skilled in the art.

As one example, the systems and methods identify the patient as a pumpuser and may further identify the type of usage of the pump, selectedfrom: open loop (evaluates basal and/or bolus/timing), semi-closed loop,and closed loop (which may be further divided, for example, intoartificial pancreas algorithm type A and artificial pancreas algorithmtype B). Other insulin pump strategies may be identified as appreciatedby one skilled in the art, including programmable basal and bolussettings, both in terms of timing and amount, as well as combinationbasal-bolus therapies recommended by particular programs or providers.

As another example, the systems and methods identify whether the patientboluses, and if so, what behavioral strategy is associated with theirregular bolus pattern. One bolus pattern used by some patients includesa fixed time of day bolusing strategy (i.e., bolusing at specific timesof day), meal-time bolusing, carb counting bolusers (e.g., wherein thepatient regularly enters different carb amounts at most meals), non-carbcounting bolusers (e.g., wherein the patient estimates (S/M/L)),pre-meal bolusing (dosing first and then titrating food), micro-boluserbolusing (e.g., bolusing more than x times per day on average (where xis greater than 5, 6, 7, or more)), and the like as is appreciated byone skilled in the art.

Other examples include sliding scale bolusing, wherein systems andmethods identify whether the patient boluses responsive to BG above acertain range. Other philosophies for insulin management may beconsidered as may be appreciated by one skilled in the art.

At 1640, the strategy is optionally scored for patient compliance withthat strategy. The insulin strategy scorer (compliance scorer) 1530quantifies the patient's compliance with the identified insulinstrategy, how strictly the patient adheres to the identified insulinstrategy. The score is computed for the patient's degrees of compliancewith the strategy, which may be used as a gate keeper to determinewhether or how to proceed to the next step. For example, if thecompliance score is above a given threshold level, then processing nextstep, else additional analysis, patient query, or feedback to adifferent algorithm (e.g., FIG. 8) may be performed.

At 1650, optimization is performed for the identified strategy. In otherwords, the optimization is limited by the bounds of the patient'spreferred diabetes management regime without requiring behaviormodification. While not wishing to be bound by theory, by optimizinginsulin therapy informed by patient behavior, more efficient andeffective therapy optimization may be achieved.

In general, the insulin strategy adapter (insulin strategy optimizerwithin the identified behavior 1540) adapts the insulinoptimization/recommendation based on the selected insulin strategyand/or based on the score associated with the insulin strategy. In someembodiments, the optimization iteratively proposes percentage changes tothe parameters of the strategy in a selected therapeutic zone or zonegroup, after which the improvement(s) may be quantified in a feedbackloop fashion until a certain improvement is achieved and/or no moreimprovement through iterative change can be seen in the quantification.

In some embodiments, the optimization may use the replay-predictivefunction (e.g., described in U.S. application Ser. No. 17/096,785,entitled “Joint state estimation prediction that evaluates differencesin predicted vs. corresponding received data”, filed Nov. 12, 2020,inventor Stephen D. Patek, which is incorporated by reference herein inits entirety) to estimate the impact on historical BG. In theseembodiments, the risk profiling function may re-run on each iterativeoptimization and the percentage improvement/change in BG outcome metricsassessed until a certain criteria is met.

At 1660, the optimized insulin strategy parameters are outputted to auser interface or a connected device e.g., via a therapy identifieroptimizer report generator 1550. In an implementation, the therapyidentifier optimizer report generator 1550 provides an output 1560, suchas outputting candidate therapy change to a user (clinician, patient, orconnected device/system). The output 1560 may be in the form of a graphillustrating the optimized insulin strategy parameters. In someimplementations, one or more parameters may be output to a userinterface or connected device, such as a clinician report or connectedbolus calculator. Additionally or alternatively, the output may beprovided by a natural language processor to describe the candidatechange and optimized risk outcome. The output may identify which timewindows have been optimized and, in some embodiments a text generatormay be used to communicate the result(s).

An example of insulin strategy optimizer 1510 is now described on onedata set from a patient with type 1 diabetes. In this example, historicCGM data was analyzed to identify nocturnal hyperglycemia as anaddressable risk profile. FIG. 17 is a chart 1700 that shows 10 days ofCGM data for the patient. Although there is one outlier day withmid-morning hypoglycemia and significant rebound on one day (indicatedby line 1710), a systematic pattern of nocturnal hypoglycemia can beseen in the majority of the traces (indicated by lines 1720). This is acombination example wherein the replayer is built, therapeutic zonesidentified, strategic adjustments made (e.g., percentage change instrategic parameter (or sweep of parameters)) and then replayed to findoptimal glycemic outcome.

FIG. 18 is a chart 1800 that illustrates the risk profile functionoutput from the analysis of the 10 days of CGM data show FIG. 17. Therisk profile output confirms the consistent exposure to hyperglycemiaovernight indicative of enhanced hyperglycemia risk (shaded region 1810above the x-axis). Some exposure to daytime hypoglycemia is shown ashypoglycemia risk (shaded region 1820 below the x-axis). Accordingly,the therapeutic improvement opportunity is identified from thehyperglycemia risk profile above as nocturnal hyperglycemia especiallyduring the time period wherein the risk profile value is above 2, about1800 hours to about 2400 hours. This risk is addressable because thereis no concurrent hypoglycemia adjacent to the nocturnal hyperglycemia.

Upon analysis of the concurrent insulin data (provided by basal andbolus data) and meal data (provided by acknowledged carbohydrate data),the patient is identified as using a functional insulin therapy(basal-bolus therapy), bolusing at times of acknowledged carbohydrates.From the data, the nominal parameters can be identified, includingprevailing basal prescription and current correction factor andcarbohydrate ratio programmed into the bolus calculator

In this example, the insulin strategy optimizer utilizes the replaysimulation (e.g., described in U.S. application Ser. No. 17/096,785,entitled “Joint state estimation prediction that evaluates differencesin predicted vs. corresponding received data”, filed Nov. 12, 2020,inventor Stephen D. Patek, which is incorporated by reference herein inits entirety). The replay simulates carbohydrates as historicallyacknowledged by the patient during data collection. Boluses aresimulated only at times of acknowledged carbohydrates. Discordancebetween the replay simulation and historical data is expected becauseboluses are not simulated at times of historical boluses. Simulateddoses are computed strictly according to the prevailing simulated BG,IOB (insulin on board), and acknowledged carbohydrates and the currentprescription of carbohydrate ratio and correction factor. This may bereferred to as compliant functional insulin therapy.

FIG. 19 are charts 1900, 1950 that illustrate a comparison of riskprofiles from the historical data and from replay simulation. Thus, forthis example, one would only expect to see a close match betweensimulated CGM traces and historical CGM if the patient (1) eats exactlyaccording to the carbohydrate announcements and (2) only boluses at mealtimes. In this example, historical boluses may be delayed and estimatesof carbohydrates and insulin on board may be inaccurate. Patientcompliance may be optionally scored here to determine compliance withthe function insulin therapy.

Next, replay simulations are run on the simulated CGM traces for a largenumber of candidate parameter settings, here using a reduced basalinsulin dose. The parameters that do the most to minimize the patient's(replayed) exposure to hypoglycemic risk and hyperglycemic risk arestored for future reference. The risk profiles and therapeutic zones arere-run. FIG. 20 illustrates a risk profile 2000 from a replay.

As an example, FIG. 21 is a chart 2100 showing two hyperglycemic riskprofiles 2120. Similar to the nominal case, the zones are smallerbecause the higher basal done leads to less time with BG in thehyperglycemic range. As another example, FIG. 22 is a chart 2200 showingtwo corresponding hyperglycemic therapeutic zones 2220.

Further optimization can be provided for carbohydrate ratios andcorrection factors. Replay simulations are run for a large number ofcandidate parameter settings, here using an increased basal insulindose. The parameters that do the most to minimize the patient's(replayed) exposure to hypoglycemisk risk and hyperglycemic risk arestored for future reference.

After the various optimizations have run and been quantified, the bestcombination of parameters are selected. FIG. 23 is a chart 2300 showingoptimized bolus and basal parameters.

FIGS. 24 and 25 illustrate charts that show example risk profiles 2400,2500, 2550. The risk profiles show that by applying the optimizedparameters, the patient could have reduced their glycemic risksignificantly, and therefore recommendations for the optimizedparameters can be outputted as described in more detail herein.

In another example, wherein a patient that prefers fixed time-of-daybolusing insulin strategy, the systems and methods may receive CGM, andinsulin amount and time, and identify a therapeutic opportunity asafternoon hypoglycemia for a time window (e.g., time x to time y). Theinsulin strategy is identified as fixed time of day bolusing based on apattern identified in regular time of day of bolusing patterns. Theinsulin strategy scorer identifies a correlation with specific times ofday of 85%. The insulin strategy adaptation iteratively runs percentagechanges in time and amount of insulin bolusing and recommends bolusingfor lunch 30 minutes sooner and/or uses 10% more insulin at normalmidday bolus. The report out to patient indicates that a 30 minute shiftin timing and/or 10% increase in midday fixed bolus would produce a 20%reducing in hypoglycemia, and the combination of both would produce a25% decrease in hypoglycemia.

In yet another example, a patient that prefers mealtime bolusing withoutcounting carbs likely uses a small, medium, large (S/M/L) mealestimation instead based on three typical bolus amounts. In thisexample, the data inputs include CGM, insulin amount and time, and mealtime. The therapeutic opportunity is identified by a patient requestinga “bolus check-up” from the user interface. The insulin strategyidentified is a mealtime bolusing strategy with three typical dosesindicative of S/M/L meal estimation. The insulin strategy scorer shows acorrelation with a typical carb estimator for the S/M/L estimation. Theinsulin strategy adaptation recommends an increase in medium boluses by10% and/or decrease large boluses by 10%. The output reports to thepatient that an increase in medium-sized boluses by 10% and a decreaseof large boluses by 10% would produce an decrease in hypoglycemia andhyperglycemia, and wherein the combination of both would produce aparticular percentage amount decrease (x % where x is a determined,calculated, or estimated number) in hypoglycemia and a particularpercentage amount decrease (x %) in hyperglycemia.

FIG. 26 shows an exemplary computing environment in which exampleembodiments and aspects may be implemented. The computing deviceenvironment is only one example of a suitable computing environment andis not intended to suggest any limitation as to the scope of use orfunctionality.

Numerous other general purpose or special purpose computing devicesenvironments or configurations may be used. Examples of well-knowncomputing devices, environments, and/or configurations that may besuitable for use include, but are not limited to, personal computers,server computers, handheld or laptop devices, multiprocessor systems,microprocessor-based systems, network personal computers (PCs),minicomputers, mainframe computers, embedded systems, distributedcomputing environments that include any of the above systems or devices,and the like.

Computer-executable instructions, such as program modules, beingexecuted by a computer may be used. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data types.Distributed computing environments may be used where tasks are performedby remote processing devices that are linked through a communicationsnetwork or other data transmission medium. In a distributed computingenvironment, program modules and other data may be located in both localand remote computer storage media including memory storage devices.

With reference to FIG. 26, an exemplary system for implementing aspectsdescribed herein includes a computing device, such as computing device2600. In its most basic configuration, computing device 2600 typicallyincludes at least one processing unit 2602 and memory 2604. Depending onthe exact configuration and type of computing device, memory 2604 may bevolatile (such as random access memory (RAM)), non-volatile (such asread-only memory (ROM), flash memory, etc.), or some combination of thetwo. This most basic configuration is illustrated in FIG. 26 by dashedline 2606.

Computing device 2600 may have additional features/functionality. Forexample, computing device 2600 may include additional storage (removableand/or non-removable) including, but not limited to, magnetic or opticaldisks or tape. Such additional storage is illustrated in FIG. 26 byremovable storage 2608 and non-removable storage 2610.

Computing device 2600 typically includes a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by the device 2600 and includes both volatile and non-volatilemedia, removable and non-removable media.

Computer storage media include volatile and non-volatile, and removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules or other data. Memory 2604, removablestorage 2608, and non-removable storage 2610 are all examples ofcomputer storage media. Computer storage media include, but are notlimited to, RAM, ROM, electrically erasable program read-only memory(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computing device 2600. Any such computerstorage media may be part of computing device 2600.

Computing device 2600 may contain communication connection(s) 2612 thatallow the device to communicate with other devices. Computing device2600 may also have input device(s) 2614 such as a keyboard, mouse, pen,voice input device, touch input device, etc. Output device(s) 2616 suchas a display, speakers, printer, etc. may also be included. All thesedevices are well known in the art and need not be discussed at lengthhere.

In an implementation, a method comprises: deriving at least one singlesymptom-specific risk profile, using a glycemic risk profiler;determining, using a therapeutic zone assessor, at least onetherapeutically correlated zone associated with the at least one singlesymptom-specific risk profile; determining, using a zone importancequantifier, an importance value of the at least one therapeuticallycorrelated zone; and outputting information based on the importancevalue.

Implementations may include some or all of the following features. Themethod further comprises receiving glucose data, wherein deriving the atleast one single symptom-specific risk profile uses the glucose data.The glucose data comprises at least one of CGM (continuous glucosemonitoring) readings, confidence readings assigned to CGM values,self-monitoring blood glucose readings, or retrospectively calibrated orcorrected CGM readings. The glucose data encompasses a time period of atleast one week. The at least one single symptom-specific risk profiledescribes either hypoglycemic risk or hyperglycemic risk as a functionof the time of day using glucose data. Deriving the at least one singlesymptom-specific risk profile comprises at least one of evaluatingsteepness (first and second order derivatives of a curve), frequency,severity, curvature, average value of profile across 24 hours, orvariability of the profile (mean and standard deviation). The at leastone single symptom-specific risk profile is indicative of glycemicdysfunction based the CGM signal over a selected time period, indicatingrecurring windows of time characterized by a predefined severity andfrequency of hypoglycemia or hyperglycemia over the selected timeperiod. The at least one single symptom-specific risk profile representsat least one of hypoglycemia isolated from hyperglycemia, orhyperglycemia isolated from hypoglycemia. Determining the at least onetherapeutically correlated zone comprises identifying the at least onetherapeutically correlated zone from the at least one singlesymptom-specific risk profile. The at least one therapeuticallycorrelated zone is an interval of a 24 hour day in which BG data of apatient indicates that at least one of the insulin basal rate or dose orbolus strategies of the patient are systematically non-optimal. The atleast one therapeutically correlated zone is identified and associatedwith at least one risk profile and comprises at least one interval ofthe day in which one or more single symptom-specific risk profilesindicate potential glycemic dysfunction.

Implementations may also include some or all of the following features.The method further comprises identifying a period of time in which theat least one single symptom-specific risk profile could be mitigated viathe adjustment of parameters or timing of insulin therapy. Determiningthe at least one therapeutically correlated zone is based on which ofleast one of candidate behavioral changes or therapeutic changes arepredicted to decrease a single-symptom glycemic risk without asubsequent increase in another symptom. Determining the importance valueof the at least one therapeutically correlated zone comprisesprioritizing the zone that is more therapeutically significant oraddressable. Determining the importance value of the at least onetherapeutically correlated zone comprises evaluating a magnitude of arisk. Determining the importance value of the at least onetherapeutically correlated zone comprises considering at least one ofthe time of day or proximity of one risk profile to another riskprofile. The importance value is the peak value of the at least onesingle symptom-specific risk profile. Deriving the at least one singlesymptom-specific risk profile is based on data above a particularcredibility level. Outputting the information comprises outputting atleast one of numerical, alphanumerical, or graphical information.Outputting the information comprises outputting at least one ofbehavioral changes or therapeutic changes to a therapeutic zone of timeto decrease a single symptom in a time window. Outputting theinformation comprises outputting the information to a connected insulinpump or insulin pen, or into a bolus calculator. Outputting theinformation comprises outputting a graphical representation of at leastone of risk profiles or therapeutically correlated zones, or relativeimportance of at least one of risk profiles or therapeuticallycorrelated zones.

In an implementation, a system comprises: a glycemic risk profilerconfigured to derive at least one single symptom-specific risk profile;a therapeutic zone assessor configured to determine at least onetherapeutically correlated zone associated with the at least one singlesymptom-specific risk profile; a zone importance quantifier configuredto determine an importance value of the at least one therapeuticallycorrelated zone; and a therapeutic zone report generator configured tooutput information based on the importance value.

Implementations may include some or all of the following features. Theglycemic risk profiler is further configured to receive glucose data,wherein deriving the at least one single symptom-specific risk profileuses the glucose data. The glucose data comprises at least one of CGM(continuous glucose monitoring) readings, confidence readings assignedto CGM values, self-monitoring blood glucose readings, orretrospectively calibrated or corrected CGM readings. The glucose dataencompasses a time period of at least one week. The at least one singlesymptom-specific risk profile describes either hypoglycemic risk orhyperglycemic risk as a function of the time of day using glucose data.Deriving the at least one single symptom-specific risk profile comprisesat least one of evaluating steepness (first and second order derivativesof a curve), frequency, severity, curvature, average value of profileacross 24 hours, or variability of the profile (mean and standarddeviation). The at least one single symptom-specific risk profile isindicative of glycemic dysfunction based the CGM signal over a selectedtime period, indicating recurring windows of time characterized by apredefined severity and frequency of hypoglycemia or hyperglycemia overthe selected time period. The at least one single symptom-specific riskprofile represents at least one of hypoglycemia isolated fromhyperglycemia, or hyperglycemia isolated from hypoglycemia. Determiningthe at least one therapeutically correlated zone comprises identifyingthe at least one therapeutically correlated zone from the at least onesingle symptom-specific risk profile. The at least one therapeuticallycorrelated zone is an interval of a 24 hour day in which BG data of apatient indicates that at least one of the insulin basal rate or dose orbolus strategies of the patient are systematically non-optimal. The atleast one therapeutically correlated zone is identified and associatedwith at least one risk profile and comprises at least one interval ofthe day in which one or more single symptom-specific risk profilesindicate potential glycemic dysfunction.

Implementations may also include some or all of the following features.The therapeutic zone assessor is further configured to identify a periodof time in which the at least one single symptom-specific risk profilecould be mitigated via the adjustment of parameters or timing of insulintherapy. Determining the at least one therapeutically correlated zone isbased on which of least one of candidate behavioral changes ortherapeutic changes are predicted to decrease a single-symptom glycemicrisk without a subsequent increase in another symptom. Determining theimportance value of the at least one therapeutically correlated zonecomprises prioritizing the zone that is more therapeutically significantor addressable. Determining the importance value of the at least onetherapeutically correlated zone comprises evaluating a magnitude of arisk. Determining the importance value of the at least onetherapeutically correlated zone comprises considering at least one ofthe time of day or proximity of one risk profile to another riskprofile. The importance value is the peak value of the at least onesingle symptom-specific risk profile. Deriving the at least one singlesymptom-specific risk profile is based on data above a particularcredibility level. Outputting the information comprises outputting atleast one of numerical, alphanumerical, or graphical information.Outputting the information comprises outputting at least one ofbehavioral changes or therapeutic changes to a therapeutic zone of timeto decrease a single symptom in a time window. Outputting theinformation comprises outputting the information to a connected insulinpump or insulin pen, or into a bolus calculator. Outputting theinformation comprises outputting a graphical representation of at leastone of risk profiles or therapeutically correlated zones, or relativeimportance of at least one of risk profiles or therapeuticallycorrelated zones.

In an implementation, a system comprises: at least one processor; and anon-transitory computer readable medium comprising instructions that,when executed by the at least one processor, cause the system to: deriveat least one single symptom-specific risk profile; determine at leastone therapeutically correlated zone associated with the at least onesingle symptom-specific risk profile; determine an importance value ofthe at least one therapeutically correlated zone; and output informationbased on the importance value.

Implementations may include some or all of the following features. Thesystem further comprises instructions that, when executed by the atleast one processor, cause the system to receive glucose data, whereinderiving the at least one single symptom-specific risk profile uses theglucose data. The glucose data comprises at least one of CGM (continuousglucose monitoring) readings, confidence readings assigned to CGMvalues, self-monitoring blood glucose readings, or retrospectivelycalibrated or corrected CGM readings. The glucose data encompasses atime period of at least one week. The at least one singlesymptom-specific risk profile describes either hypoglycemic risk orhyperglycemic risk as a function of the time of day using glucose data.Deriving the at least one single symptom-specific risk profile comprisesat least one of evaluating steepness (first and second order derivativesof a curve), frequency, severity, curvature, average value of profileacross 24 hours, or variability of the profile (mean and standarddeviation). The at least one single symptom-specific risk profile isindicative of glycemic dysfunction based the CGM signal over a selectedtime period, indicating recurring windows of time characterized by apredefined severity and frequency of hypoglycemia or hyperglycemia overthe selected time period. The at least one single symptom-specific riskprofile represents at least one of hypoglycemia isolated fromhyperglycemia, or hyperglycemia isolated from hypoglycemia. Determiningthe at least one therapeutically correlated zone comprises identifyingthe at least one therapeutically correlated zone from the at least onesingle symptom-specific risk profile. The at least one therapeuticallycorrelated zone is an interval of a 24 hour day in which BG data of apatient indicates that at least one of the insulin basal rate or dose orbolus strategies of the patient are systematically non-optimal. The atleast one therapeutically correlated zone is identified and associatedwith at least one risk profile and comprises at least one interval ofthe day in which one or more single symptom-specific risk profilesindicate potential glycemic dysfunction.

Implementations may also include some or all of the following features.The system further comprises instructions that, when executed by the atleast one processor, cause the system to identify a period of time inwhich the at least one single symptom-specific risk profile could bemitigated via the adjustment of parameters or timing of insulin therapy.Determining the at least one therapeutically correlated zone is based onwhich of least one of candidate behavioral changes or therapeuticchanges are predicted to decrease a single-symptom glycemic risk withouta subsequent increase in another symptom. Determining the importancevalue of the at least one therapeutically correlated zone comprisesprioritizing the zone that is more therapeutically significant oraddressable. Determining the importance value of the at least onetherapeutically correlated zone comprises evaluating a magnitude of arisk. Determining the importance value of the at least onetherapeutically correlated zone comprises considering at least one ofthe time of day or proximity of one risk profile to another riskprofile. The importance value is the peak value of the at least onesingle symptom-specific risk profile. Deriving the at least one singlesymptom-specific risk profile is based on data above a particularcredibility level. Outputting the information comprises outputting atleast one of numerical, alphanumerical, or graphical information.Outputting the information comprises outputting at least one ofbehavioral changes or therapeutic changes to a therapeutic zone of timeto decrease a single symptom in a time window. Outputting theinformation comprises outputting the information to a connected insulinpump or insulin pen, or into a bolus calculator. Outputting theinformation comprises outputting a graphical representation of at leastone of risk profiles or therapeutically correlated zones, or relativeimportance of at least one of risk profiles or therapeuticallycorrelated zones.

In an implementation, a method comprises: receiving glucose and insulindata; identifying a therapeutic improvement opportunity using theglucose and insulin data; determining candidate changes to insulintherapy; assessing an improvement in therapeutic risk based on thecandidate changes; quantifying the improvement of the candidate changes;and outputting at least one of the candidate changes based on theimprovement.

Implementations may include some or all of the following features. Theglucose and insulin data is received from at least one of a patient or aconnected system or device. Identifying the therapeutic improvementopportunity comprises receiving a user selection of at least one of amealtime, a time of day, or a parameter setting. The parameter settingis a carb ratio. The candidate changes to insulin therapy comprisepercentage increases or decreases to bolus therapy or basal therapy. Thecandidate changes to insulin therapy comprise changes to insulindelivery parameters associated with bolus therapy or basal therapy. Thecandidate changes are in terms of carb ratios, correction factors, basalrates, or profiles. The candidate changes comprise basal dosesensitivity. The candidate changes comprise percentage change to basalor bolus doses in therapeutic zones. Quantifying the improvement of thecandidate changes comprises comparing risk profile values. Outputting atleast one of the candidate changes based on the improvement comprisesoutputting the candidate change that provides the optimized riskprofile. Outputting at least one of the candidate changes comprisesproviding an output in the form of a graph illustrating at least one ofa candidate change or an optimized risk output to a user interface orconnected device. The connected device comprises a bolus calculator. Theoutput is provided by a natural language processor to describe acandidate change and an optimized risk outcome. The output identifieswhich therapeutic zones or zone groups have been optimized.

In an implementation, a system comprises: a therapeutic improvementidentifier configured to evaluate collated glucose and insulin data of apatient to identify areas for therapy optimization in a diabetesmanagement routine of the patient, and to generate a therapeuticimprovement; a relative insulin optimizer configured to propose changesto a therapy, assess the impact of the changes, and quantifies animprovement associated with the changes; and a relative insulinoptimizer report generator that provides an output.

Implementations may include some or all of the following features. Therelative insulin optimizer comprises: a change proposer configured topropose the changes to insulin therapy; an impact assessor configured toassess the impact of candidate therapy changes by estimating the impactto a risk profile of historical glucose values; and an improvementquantifier configured to quantify an improvement of candidate therapychanges. The change proposer is further configured to propose thechanges as percentage-wise changes to at least one of basal or bolus ina time window. The improvement quantifier is configured to quantify theimprovement of candidate therapy changes, based on a percentageimprovement or change in blood glucose outcome metrics. The relativeinsulin optimizer report generator is configured to output candidatetherapy change to a user. The user is one of a clinician, a patient, ora connected device or system. The therapeutic improvement identifiercomprises a user selection of a therapy or a time of day to beoptimized. The user is a patient or a clinician. The therapeuticimprovement is identified by an algorithm.

In an implementation, a system comprises: at least one processor; and anon-transitory computer readable medium comprising instructions that,when executed by the at least one processor, cause the system to:receive glucose and insulin data; identify a therapeutic improvementopportunity using the glucose and insulin data; determine candidatechanges to insulin therapy; assess an improvement in therapeutic riskbased on the candidate changes; quantify the improvement of thecandidate changes; and output at least one of the candidate changesbased on the improvement.

Implementations may include some or all of the following features. Theglucose and insulin data is received from at least one of a patient or aconnected system or device. Identifying the therapeutic improvementopportunity comprises receiving a user selection of at least one of amealtime, a time of day, or a parameter setting. The parameter settingis a carb ratio. The candidate changes to insulin therapy comprisepercentage increases or decreases to bolus therapy or basal therapy. Thecandidate changes to insulin therapy comprise changes to insulindelivery parameters associated with bolus therapy or basal therapy. Thecandidate changes are in terms of carb ratios, correction factors, basalrates, or profiles. The candidate changes comprise basal dosesensitivity. The candidate changes comprise percentage change to basalor bolus doses in therapeutic zones. Quantifying the improvement of thecandidate changes comprises comparing risk profile values. Outputting atleast one of the candidate changes based on the improvement comprisesoutputting the candidate change that provides the optimized riskprofile. Outputting at least one of the candidate changes comprisesproviding an output in the form of a graph illustrating at least one ofa candidate change or an optimized risk output to a user interface orconnected device. The connected device comprises a bolus calculator. Theoutput is provided by a natural language processor to describe acandidate change and an optimized risk outcome. The output identifieswhich therapeutic zones or zone groups have been optimized.

In an implementation, a method comprises: receiving at least one ofglucose data, insulin data, or other-diabetes related data of a patient;identifying a therapeutic improvement opportunity using the at least oneof glucose data, insulin data, or other-diabetes related data;identifying an insulin dosing strategy of the patient; scoring theinsulin dosing strategy for patient compliance; performing optimizationfor the insulin dosing strategy; and providing an output comprisingoptimized insulin strategy parameters to a user.

Implementations may include some or all of the following features. Theother diabetes-related data comprises at least one of meal information,specificity of meals, timing of meals, sizing of meals, carbohydrateestimates, composition information, or exercise information. The atleast one of glucose data, insulin data, or other-diabetes related datais received from at least one of a patient or a connected system ordevice. Identifying the therapeutic improvement opportunity comprisesreceiving a user selection of at least one of a mealtime, a time of day,or a parameter setting. The parameter setting is a carb ratio. Theinsulin dosing strategy comprises a diabetes management or insulinstrategy being implemented by the patient in practice as determined fromthe at least one of glucose data, insulin data, or other-diabetesrelated data of a patient. Performing optimization for the insulindosing strategy determines whether the patient adheres to a knowninsulin strategy and analyzes the effect of percentage changes to theparameters of the identified insulin strategy. The user is at least oneof a clinician, a patient, or a connected device or system. The outputis provided by a natural language processor to describe a candidatechange and an optimized risk outcome. Providing the output comprisesproviding an output in the form of a graph illustrating the optimizedinsulin strategy parameters to a user interface or connected device. Theconnected device comprises a bolus calculator.

In an implementation, a system comprises: a therapeutic improvementidentifier configured to evaluate collated glucose and insulin data of apatient to identify areas for therapy optimization in a diabetesmanagement routine of the patient, and to generate a therapeuticimprovement; an insulin strategy optimizer configured to determinewhether the patient adheres to a known insulin strategy and analyze theeffect of percentage changes to the parameters of the identified insulinstrategy; and a therapy identifier optimizer report generator thatprovides an output.

Implementations may include some or all of the following features. Theinsulin strategy optimizer comprises: an insulin strategy identifierconfigured to identify a diabetes management or insulin strategy beingimplemented by the patient in practice as determined from the collatedglucose and insulin data; a compliance scorer configured to quantify acompliance of the patient with the identified insulin strategy; and aninsulin strategy optimizer within identified behavior configured toperform optimization for the identified insulin strategy. The diabetesdata comprises insulin data and meal data. The insulin strategyidentifier is configured to identify patterns in dosing andcharacterizes the identified insulin strategy of the patient basedthereon. The insulin strategy is the behavioral methodology that thepatient applies in diabetes management, comprising at least one of typesof insulin pump usage, multiple daily injections, or type 2 therapies.The compliance scorer is configured to generate a score computed for adegree of compliance of the patient with the identified insulinstrategy. The insulin strategy optimizer within identified behavior isconfigured to iteratively propose percentage changes to the parametersof the strategy in a selected therapeutic zone or zone group. The outputcomprises optimized insulin strategy parameters. The therapy identifieroptimizer report generator is configured to output a candidate therapychange to a user. The user is at least one of a clinician, a patient, ora connected device or system. The output is provided by a naturallanguage processor to describe a candidate change and an optimized riskoutcome.

In an implementation, a system comprises: at least one processor; and anon-transitory computer readable medium comprising instructions that,when executed by the at least one processor, cause the system to:receive at least one of glucose data, insulin data, or other-diabetesrelated data of a patient; identify a therapeutic improvementopportunity using the at least one of glucose data, insulin data, orother-diabetes related data; identify an insulin dosing strategy of thepatient; score the insulin dosing strategy for patient compliance;perform optimization for the insulin dosing strategy; and provide anoutput comprising optimized insulin strategy parameters to a user.

Implementations may include some or all of the following features. Theother diabetes-related data comprises at least one of meal information,specificity of meals, timing of meals, sizing of meals, carbohydrateestimates, composition information, or exercise information. The atleast one of glucose data, insulin data, or other-diabetes related datais received from at least one of a patient or a connected system ordevice. Identifying the therapeutic improvement opportunity comprisesreceiving a user selection of at least one of a mealtime, a time of day,or a parameter setting. The parameter setting is a carb ratio. Theinsulin dosing strategy comprises a diabetes management or insulinstrategy being implemented by the patient in practice as determined fromthe at least one of glucose data, insulin data, or other-diabetesrelated data of a patient. Performing optimization for the insulindosing strategy determines whether the patient adheres to a knowninsulin strategy and analyzes the effect of percentage changes to theparameters of the identified insulin strategy. The user is at least oneof a clinician, a patient, or a connected device or system. The outputis provided by a natural language processor to describe a candidatechange and an optimized risk outcome. Providing the output comprisesproviding an output in the form of a graph illustrating the optimizedinsulin strategy parameters to a user interface or connected device. Theconnected device comprises a bolus calculator.

It should be understood that the various techniques described herein maybe implemented in connection with hardware components or softwarecomponents or, where appropriate, with a combination of both.Illustrative types of hardware components that can be used includeField-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (AS SPs),System-on-a-chip systems (SOCs), Complex Programmable Logic Devices(CPLDs), etc. The methods and apparatus of the presently disclosedsubject matter, or certain aspects or portions thereof, may take theform of program code (i.e., instructions) embodied in tangible media,such as floppy diskettes, CD-ROMs, hard drives, or any othermachine-readable storage medium where, when the program code is loadedinto and executed by a machine, such as a computer, the machine becomesan apparatus for practicing the presently disclosed subject matter.

Although exemplary implementations may refer to utilizing aspects of thepresently disclosed subject matter in the context of one or morestand-alone computer systems, the subject matter is not so limited, butrather may be implemented in connection with any computing environment,such as a network or distributed computing environment. Still further,aspects of the presently disclosed subject matter may be implemented inor across a plurality of processing chips or devices, and storage maysimilarly be effected across a plurality of devices. Such devices mightinclude personal computers, network servers, and handheld devices, forexample.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method comprising: receiving at least one ofglucose data, insulin data, or other-diabetes related data of a patient;identifying a therapeutic improvement opportunity using the at least oneof glucose data, insulin data, or other-diabetes related data;identifying an insulin dosing strategy of the patient; scoring theinsulin dosing strategy for patient compliance; performing optimizationfor the insulin dosing strategy; and providing an output comprisingoptimized insulin strategy parameters to a user.
 2. The method of claim1, wherein the other diabetes-related data comprises at least one ofmeal information, specificity of meals, timing of meals, sizing ofmeals, carbohydrate estimates, composition information, or exerciseinformation.
 3. The method of claim 1, wherein the at least one ofglucose data, insulin data, or other-diabetes related data is receivedfrom at least one of a patient or a connected system or device.
 4. Themethod of claim 1, wherein identifying the therapeutic improvementopportunity comprises receiving a user selection of at least one of amealtime, a time of day, or a parameter setting.
 5. The method of claim4, wherein the parameter setting is a carb ratio.
 6. The method of claim1, wherein the insulin dosing strategy comprises a diabetes managementor insulin strategy being implemented by the patient in practice asdetermined from the at least one of glucose data, insulin data, orother-diabetes related data of a patient.
 7. The method of claim 1,wherein performing optimization for the insulin dosing strategydetermines whether the patient adheres to a known insulin strategy andanalyzes the effect of percentage changes to the parameters of theidentified insulin strategy.
 8. The method of claim 1, wherein the useris at least one of a clinician, a patient, or a connected device orsystem.
 9. The method of claim 1, wherein the output is provided by anatural language processor to describe a candidate change and anoptimized risk outcome.
 10. The method of claim 1, wherein providing theoutput comprises providing an output in the form of a graph illustratingthe optimized insulin strategy parameters to a user interface orconnected device.
 11. The method of claim 10, wherein the connecteddevice comprises a bolus calculator.
 12. A system comprising: atherapeutic improvement identifier configured to evaluate collatedglucose and insulin data of a patient to identify areas for therapyoptimization in a diabetes management routine of the patient, and togenerate a therapeutic improvement; an insulin strategy optimizerconfigured to determine whether the patient adheres to a known insulinstrategy and analyze the effect of percentage changes to the parametersof the identified insulin strategy; and a therapy identifier optimizerreport generator that provides an output.
 13. The system of claim 12,wherein the insulin strategy optimizer comprises: an insulin strategyidentifier configured to identify a diabetes management or insulinstrategy being implemented by the patient in practice as determined fromthe collated glucose and insulin data; a compliance scorer configured toquantify a compliance of the patient with the identified insulinstrategy; and an insulin strategy optimizer within identified behaviorconfigured to perform optimization for the identified insulin strategy.14. The system of claim 13, wherein the diabetes data comprises insulindata and meal data.
 15. The system of claim 13, wherein the insulinstrategy identifier is configured to identify patterns in dosing andcharacterizes the identified insulin strategy of the patient basedthereon.
 16. The system of claim 13, wherein the insulin strategy is thebehavioral methodology that the patient applies in diabetes management,comprising at least one of types of insulin pump usage, multiple dailyinjections, or type 2 therapies.
 17. The system of claim 13, wherein thecompliance scorer is configured to generate a score computed for adegree of compliance of the patient with the identified insulinstrategy.
 18. The system of claim 13, wherein the insulin strategyoptimizer within identified behavior is configured to iterativelypropose percentage changes to the parameters of the strategy in aselected therapeutic zone or zone group.
 19. The system of claim 12,wherein the output comprises optimized insulin strategy parameters. 20.The system of claim 12, wherein the therapy identifier optimizer reportgenerator is configured to output a candidate therapy change to a user.21. The system of claim 20, wherein the user is at least one of aclinician, a patient, or a connected device or system.
 22. The system ofclaim 12, wherein the output is provided by a natural language processorto describe a candidate change and an optimized risk outcome.
 23. Asystem comprising: at least one processor; and a non-transitory computerreadable medium comprising instructions that, when executed by the atleast one processor, cause the system to: receive at least one ofglucose data, insulin data, or other-diabetes related data of a patient;identify a therapeutic improvement opportunity using the at least one ofglucose data, insulin data, or other-diabetes related data; identify aninsulin dosing strategy of the patient; score the insulin dosingstrategy for patient compliance; perform optimization for the insulindosing strategy; and provide an output comprising optimized insulinstrategy parameters to a user.
 24. The system of claim 23, wherein theother diabetes-related data comprises at least one of meal information,specificity of meals, timing of meals, sizing of meals, carbohydrateestimates, composition information, or exercise information.
 25. Thesystem of claim 23, wherein the at least one of glucose data, insulindata, or other-diabetes related data is received from at least one of apatient or a connected system or device.
 26. The system of claim 23,wherein identifying the therapeutic improvement opportunity comprisesreceiving a user selection of at least one of a mealtime, a time of day,or a parameter setting.
 27. The system of claim 26, wherein theparameter setting is a carb ratio.
 28. The system of claim 23, whereinthe insulin dosing strategy comprises a diabetes management or insulinstrategy being implemented by the patient in practice as determined fromthe at least one of glucose data, insulin data, or other-diabetesrelated data of a patient.
 29. The system of claim 23, whereinperforming optimization for the insulin dosing strategy determineswhether the patient adheres to a known insulin strategy and analyzes theeffect of percentage changes to the parameters of the identified insulinstrategy.
 30. The system of claim 23, wherein the user is at least oneof a clinician, a patient, or a connected device or system.
 31. Thesystem of claim 23, wherein the output is provided by a natural languageprocessor to describe a candidate change and an optimized risk outcome.32. The system of claim 23, wherein providing the output comprisesproviding an output in the form of a graph illustrating the optimizedinsulin strategy parameters to a user interface or connected device. 33.The system of claim 32, wherein the connected device comprises a boluscalculator.